Raman spectra may be obtained from a variety of samples and are extremely useful in helping to characterize the materials that make up the sample. Raman spectra are derived from a material's intrinsic vibrational spectroscopic signature, which is highly sensitive to the composition and structure of the material and its local chemical environment. Raman spectra and Raman images may be obtained with little or no sample preparation and are widely applicable for materials research, failure analysis, process monitoring, clinical diagnostics, forensic analysis, medical research, etc.
Raman spectra may be obtained in a number of ways, as is known in the art. As a non-limiting example, a laser photon source may be used to illuminate the sample. Photons that may be reflected, emitted, and/or scattered by the sample are collected and passed through an electronically-tunable filter, such as a liquid crystal tunable filter (“LCTF”), and acousto-optic tunable filter (“AOTF”) or other similar filters known in the art. The filtered photons may be detected by a photon detection device, such as a charge-coupled device (“CCD”). The output of the CCD may be used to form a Raman spectrum of the sample. One problem with the obtained Raman spectrum is due to fluorescence of the sample. When the sample fluoresces, the fluorescing photons may be detected by the photon detector thereby distorting the Raman spectrum obtained from the sample. This distortion may have a number of effects, one of interest is a change in the baseline of the Raman spectrum. This change may typically be seen as an elevation of the intensity of baseline data points in the Raman spectrum. This elevation of intensity may tend to obscure the peaks of the Raman spectrum thereby making it difficult for a technician or operator to obtain a correct analysis of the sample.
There exists in the art a number of methodologies for correcting the baseline of spectra. One in particular is a paper entitled “The ABC of Metabonomics Automated Baseline Correction” by Antony Williams, Sergey Golotvin, Eugene Vodopianov, and John Shockcor (the “ABC Paper”). The paper was presented during the 42nd ENC meeting in Orlando, Fla., USA between 11-16 Mar. 2001, and is incorporated herein by reference in its entirety. The paper discloses, in part, a method for baseline correction for nuclear magnetic resonance (“NMR”) spectra comprising two basic steps: baseline recognition and baseline modeling.
The baseline recognition procedure divides the points in the NMR spectrum into either “baseline” points or “peak” points. This is performed by using a sliding window centered on a particular data point. A maximum and a minimum value for the points in the window are obtained and the difference is compared with a minimal standard deviation value. The minimal standard deviation value is obtained by dividing the NMR spectrum into 32 regions and, for each of the 32 regions, calculating the standard deviation of the points in the region. The minimal standard deviation value is used for comparison with each of the sliding window difference values obtained. The baseline points will typically be separated into groups with a gap between the groups where the “peak” points are located.
In the baseline modeling procedure, the gaps between the groups of baseline points are connected with a straight line segment rather than using a polynomial fit. The resulting spectrum is then smoothed and subtracted from the NMR spectrum. The result is a baseline-corrected NMR spectrum.
While the procedure disclosed in the ABC Paper apparently works well with NMR spectra, it does not adapt very well to Raman spectra. Therefore, a need exists to apply an automated baseline correction procedure, and apparatus therefor, to Raman spectra to correct for, among other things, fluorescence effects in the Raman spectra.
Accordingly, it is an object of the present disclosure to apply a system and/or method for automated baseline correction to Raman spectra. The method and/or apparatus may be employed to correct a Raman spectrum baseline that is corrupted by, for example, fluorescence from the sample from which the Raman spectrum is obtained. In an embodiment, a first set of data points from a Raman spectrum are determined to be baseline data points and a second set of data points from the Raman spectrum are determined to be baseline data points where the second set of data points are not contiguous with the first set of data points. The gap between the first and second set of data points may be bridged by a straight line thereby forming an estimated baseline. The estimated baseline may be smoothed and then subtracted from the Raman spectrum resulting in an adjusted-baseline Raman spectrum.
In another embodiment, the determination of baseline data points includes determining a first quantity as a function of a maximum and a minimum of a value for data points in a first group of data points; determining a second quantity as a function of a signal to noise ratio of the Raman spectrum and as a function of a weighted standard deviation for the value of the data points in a second group of data points; and for ones of the data points, comparing the first quantity to the second quantity to thereby determine a first set of data points to be baseline data points.
In yet another embodiment, the determination of baseline data points includes determining the second quantity above which includes dividing the Raman spectrum into a predetermined number of sections; determining, for each section of the predetermined number of sections, a standard deviation for the value of the data points in each section; determining a weighted standard deviation from the determined standard deviation for each section of the predetermined number of sections; and multiplying the weighted standard deviation by a predetermined amount wherein the predetermined amount is a function of a signal to noise ratio of the Raman spectrum.
In still another embodiment, an apparatus for adjusting a baseline for a Raman spectrum includes means for providing a Raman spectrum with plural data points each of which has a value (such as an intensity value); first circuitry for determining a first and a second set of data points to be baseline data points where the first and second sets of data points are not contiguous; second circuitry for bridging the gap between the first and second sets of data points to thereby form an estimated baseline; filtering circuitry for smoothing the estimated baseline; and signal processing circuitry for subtracting the smoothed estimated baseline from the Raman spectrum.
In a further embodiment, a system for adjusting a baseline for a Raman spectrum including means for providing a Raman spectrum with plural data points each of which has a value (such as an intensity value); and a processor programmed to perform a plurality of executable instructions, the instructions comprising: determining a first and a second set of data points to be baseline data points where the first and second set of data points are not contiguous; bridging the gap between the first and second sets of data points to thereby form an estimated baseline; smoothing the estimated baseline; and for subtracting the smoothed estimated baseline from the Raman spectrum.
In yet a further embodiment, the above processor is programmed to perform further executable instructions, the further instructions comprising: determining the baseline data points by determining a first quantity as a function of a maximum and a minimum of a value for data points in a first group of data points; determining a second quantity as a function of a signal to noise ratio of the Raman spectrum and as a function of a weighted standard deviation for the value of the data points in a second group of data points; and for ones of the data points, comparing the first quantity to the second quantity to thereby determine a first set of data points to be baseline data points.
In still a further embodiment, the above processor is programmed to perform still further executable instructions, the still further instructions comprising: determining the second quantity by dividing the Raman spectrum into a predetermined number of sections; determining, for each section of the predetermined number of sections, a standard deviation for the value of the data points in each section; determining a weighted standard deviation from the determined standard deviation for each section of the predetermined number of sections; and multiplying the weighted standard deviation by a predetermined amount wherein the predetermined amount is a function of a signal to noise ratio of the Raman spectrum.